Graduate School of Informatics

This thesis introduces a novel loss function, the Class Distance Weighted Cross Entropy (CDW-CE) loss, for automated severity assessment of Ulcerative colitis (UC) using Convolutional Neural Networks (CNN) on endoscopic images. CDW-CE considers ordinal relationships between classes and enhances prediction accuracy, outperforming other loss functions across different metrics and architectures. It also improves class activation maps' precision, aiding explanation of model predictions. The approach's broad applicability is confirmed by successful testing on a diabetic retinopathy dataset. The study also created the largest public UC image dataset.

Date: 17.07.2023 / 12:30 Place: A-212

The study introduces a new method to classify Hepatocellular Carcinoma (HCC) using a hyperspectral imaging system (HSI) combined with a light microscope. This method leverages 3D convolutions in Convolutional Neural Networks (CNNs) to train a robust classifier, capturing unique spectral and spatial features automatically. The approach also addresses class imbalance in the dataset by employing a focal loss function, preventing overfitting. The results show that hyperspectral data surpasses RGB data in liver cancer tissue classification, and enhanced spectral resolution improves accuracy, highlighting the importance of both spectral and spatial features for effective cancer tissue classification.

Date: 19.06.2023 / 15:45 Place: B-116

This thesis proposes automatic evaluation techniques to assess mobile health applications as alternatives to manual methods. It utilizes large language models to classify applications' employed Persuasive System Design (PSD) principles from collected user reviews and application descriptions. Mobile Application Rating Scale (MARS) scores are predicted with regression models trained on classification probabilities that are enriched with additional descriptive data. The study’s proposed techniques outperform baseline models, while feature importance scores show that PSD principles have significant contributions to quality predictions of mobile applications.

Date: 20.06.2023 / 14:00 Place: A-212

This study aims to gain an understanding of the content and the epistemic causal connective acquisition process of children aged 6;5- 8 in Turkish. For this purpose, to test whether there are connectives that children use specific to domains of causality as adults do (Çokal, Zeyrek, & Sanders, 2020); child and adult participants are given both descriptive (biased for content relations) and argumentative (biased for epistemic relations) tasks. Data that is collected from these tasks are annotated and statistically analyzed.

Date: 27.04.2023 / 15:30 Place: A-212

Hepatocellular carcinoma (HCC) is a type of primary liver cancer that is highly lethal and needs better treatment options. The thesis identifies several drugs and drug combinations that show promise in targeting drug-resistant HCC cells and cancer stem cells based on in silico modeling and in vivo experiments. It found that a combination of Sunitinib and Chloroquine Phosphate is synergistically cytotoxic on HCC cells, while novel isoxazole-piperazine compounds also show bioactivities against HCC cells and cancer stem cells. The study presents significant findings that highlight the potential of repurposed drugs and novel compounds as drug candidates for HCC.

Date: 24.04.2023 / 10:30 Place: A-212

This thesis contributed to the edge computing literature by providing the first sectoral edge computing technology roadmap. It identifies how the focus of edge computing research changes by the application domain and objectives. Market and technology trends are discovered with dynamic topic modeling, using LDA and BERTopic methods. Technology roadmapping literature is extended by integrating data layer in a data-driven technology roadmap for social change and technology forecasting.

Date: 17.04.2023 / 13:00 Place: A-212

In this study, by using facial images and EMG recordings, a novel assessment method based on computation will be developed. The method to be developed will be quantified, repeatable, and easy to run autonomously on a regular computer or device.

Date: 07.04.2023 / 11:00 Place: A-212